The rolling bearing is an extremely important basic mechanical device. The diagnosis of its fault play an important role in the safe and stable operation of the mechanical system. This study proposed an approach, based on the Fast Fourier Transform (FFT) with Decimation‐In‐Time (DIT) and XGBoost algorithm, to identify the fault type of bearing quickly and accurately. Firstly, the original vibration signal of rolling bearing was transformed by DIT‐FFT and divided into the training set and test set. Next, the training set was used to train the fault diagnosis XGBoost model, and the test set was used to validate the well‐trained XGBoost model. Finally, the proposed approach was compared with some common methods. It is demonstrated that the proposed approach is able to diagnose and identify the fault type of bearing quickly with almost 99% accuracy. It is more accurate than Machine Learning (89.88%), Ensemble Learning (93.25%), and Deep Learning (95%). This approach is suitable for the fault diagnosis of rolling bearing.

Due to the influence of meteorological conditions, shipboard photovoltaic (PV) systems have problems such as large fluctuation and inaccurate prediction of the output power. In this paper, a short-term PV power prediction method based on a novel digital twin (DT) model and BiLSTM is proposed. Firstly, a PV mechanism model and a data-driven model were established, in which the data-driven model was updated iteratively in real time using the sliding time window update method; then, these two models were converged to construct a PV DT model according to the DS evidence theory. Secondly, a BiLSTM model was built to make short-term predictions of the PV power using the augmented dataset of the DT model as an input. Finally, the method was tested and verified by experiments and further compared with main PV prediction methods. The research results indicate the following: firstly, the absolute error of the DT model was smaller than that of the mechanism model and the data-driven model, being as low as 5.62 W after the data update of the data-driven model; thus, the DT model realized data augmentation and high fidelity. Secondly, compared to several main PV prediction models, the PV DT model combined with BiLSTM had the lowest RMSE, MAE, and MAPE; the best followability; and the smallest absolute error under different weather conditions, which was especially obvious under cloudy weather conditions. In summary, the method can accurately predict the shipboard PV power, which has great theoretical significance and application value for improving the economy and reliability of solar ship operation.

Aiming at the fault diagnosis accuracy of rolling bearings is not high enough, and unknown faults cannot be correctly identified. A priority elimination (PE) method is proposed in this paper. First, the priority diagnosis sequence of faults was determined by comparing the ratios of the inter-class distance to the intra-class distance for all faults. Then, the model training and fault diagnosis were carried out in order of the priority sequence, and the samples of the fault that had been identified were eliminated from the data set until all faults were diagnosed. For the diagnosis model, the stacked sparse auto-encoder network (SSAE) was selected to extract the features of the vibration signal. The extreme gradient boosting algorithm (XGBoost) was chosen to identify the fault type. Finally, the method was tested and verified by experimental data and compared with classical algorithms. Research results indicate the following: (1) with the addition of PE based on SSAE-XGBoost, the fault diagnosis accuracy can be improved from 96.3% to 99.27%, which is higher than other methods; (2) for the test set with the samples of unknown faults, the diagnosis accuracy of SSAE-XGBoost with PE can reach 92.34%, which is nearly 6% higher than that without PE and is also obviously higher than other classical fault diagnosis methods with or without PE. The PE method can not only improve the diagnosis accuracy of faults but also identify unknown faults, which provides a new method and way for fault diagnosis.

The bus voltage of the ship DC microgrid is sensitive to the change of loads, which has an influence on the power supply quality. This paper introduces a hybrid energy storage system (HESS) that is composed of a battery set and a supercapacitor set, and further studied the control method of HESS. First of all, the topological structures of the ship DC microgrid and HESS are described. Second, combined with the frequency division droop control and voltage PI control, a sliding mode control (SMC) method is proposed to control the charge and discharge of HESS based on an improved reaching law. Finally, the simulation model of the ship DC microgrid is established for the verification of the control method. Simulation results show that: (1) HESS can overcome the shortage of the dynamic response ability of the diesel rectifier generator to the steep change of load power. The supercapacitor set and the battery set successfully respond to the high-frequency and low-frequency components of the differential power in the system, respectively. (2) Compared with the traditional PI control method, SMC can reduce the current chattering of HESS and the voltage fluctuation amplitude of the DC bus. The proposed SMC method can provide a reference for the stable and reliable operation of the ship DC microgrid.

Subject to magnetic saturation, temperature rise, and other factors, the electrical parameters of permanent magnet synchronous motors (PMSMs) in marine electric propulsion systems exhibit time-varying characteristics. Existing parameter identification algorithms fail to fully satisfy the requirements of high-performance PMSM control systems in terms of accuracy, response speed, and robustness. To address these limitations, this paper introduces multi-innovation theory and proposes a novel multi-innovation extended Kalman filter (MIEKF) for the identification of key electrical parameters of PMSMs, including stator resistance, d-axis inductance, q-axis inductance, and permanent magnet flux linkage. Firstly, the extended Kalman filter (EKF) algorithm is applied to linearize the nonlinear system, enhancing the EKF’s applicability for parameter identification in highly nonlinear PMSM systems. Subsequently, multi-innovation theory is incorporated into the EKF framework to construct the MIEKF algorithm, which utilizes historical state data through iterative updates to improve the identification accuracy and dynamic response speed. An MIEKF-based PMSM parameter identification model is then established to achieve online multi-parameter identification. Finally, a StarSim RCP MT1050-based experimental platform for online PMSM parameter identification is implemented to validate the effectiveness and superiority of the proposed MIEKF algorithm under three operational conditions: no-load, speed variation, and load variation. Experimental results demonstrate that (1) across three distinct operating conditions, compared to forget factor recursive least squares (FFRLS) and the EKF, the MIEKF exhibits smaller fluctuation amplitudes, shorter fluctuation durations, mean values closest to calibrated references, and minimal deviation rates and root mean square errors in identification results; (2) under the load increase condition, the EKF shows significantly increased deviation rates while the MIEKF maintains high identification accuracy and demonstrates enhanced anti-interference ability. This research has achieved a comprehensive improvement in parameter identification accuracy, dynamic response speed, convergence effect, and anti-interference performance, providing an electrical parameter identification method characterized by high accuracy, rapid dynamic response, and strong robustness for high-performance control of PMSMs in marine electric propulsion systems.

Due to the high penetration of virtual power plants (VPPs), the bi-directional power flow between VPPs and active distribution grids makes the grid operation complex. Without congestion management, the operation schedule only considers the economic benefits, and power flow constraints might be violated. Hence, it is necessary to conduct power interaction within the operation constraints. This paper proposes a coordinated economic dispatch method under voltage security constraints. The linear expressions were derived by simplifying the AC power flow equations to reduce the computation complicity. Then, optimal economic dispatch models with voltage security constraints were established for the active distribution grid and VPPs, respectively. Meanwhile, the transacted power and clearing price were set as the communication variables, and a coordinated strategy was proposed for the overall optimal goal. The modified IEEE 33-node and PG&E-node distribution grids were utilized for the simulations, and the results affirmed the validity of the proposed method.